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1 #!/home/jjjjia/.conda/envs/py36/bin/python
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2
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3 #$ -S /home/jjjjia/.conda/envs/py36/bin/python
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4 #$ -V # Pass environment variables to the job
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5 #$ -N CPO_pipeline # Replace with a more specific job name
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6 #$ -wd /home/jjjjia/testCases # Use the current working dir
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7 #$ -pe smp 1 # Parallel Environment (how many cores)
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8 #$ -l h_vmem=11G # Memory (RAM) allocation *per core*
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9 #$ -e ./logs/$JOB_ID.err
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10 #$ -o ./logs/$JOB_ID.log
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11 #$ -m ea
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12 #$ -M bja20@sfu.ca
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13
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14 # >python cpo_galaxy_tree.py -t /path/to/tree.ph -d /path/to/distance/matrix -m /path/to/metadata
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15 # python cpo_galaxy_tree.py -t tree.txt -d ./dist.tabular -m ./metadata.tsv
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16
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17 # <requirements>
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18 # <requirement type="package" version="0.23.4">pandas</requirement>
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19 # <requirement type="package" version="3.6">python</requirement>
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20 # <requirement type="package" version="3.1.1">ete3</requirement>
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21 # <requirement type="package" version="5.9.3">pyqt</requirement>
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22 # </requirements>
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23
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24 import subprocess
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25 import pandas #conda pandas
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26 import optparse
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27 import os
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28 import datetime
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29 import sys
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30 import time
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31 import urllib.request
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32 import gzip
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33 import collections
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34 import json
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35 import numpy #conda numpy
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36 import ete3 as e #conda ete3 3.1.1**** >requires pyqt5
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37 import csv
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38
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39 #parses some parameters
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40 parser = optparse.OptionParser("Usage: %prog [options] arg1 arg2 ...")
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41 parser.add_option("-t", "--tree", dest="treePath", type="string", default="./pipelineTest/tree.txt", help="identifier of the isolate")
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42 parser.add_option("-d", "--distance", dest="distancePath", type="string", default="./pipelineTest/distance.tab", help="absolute file path forward read (R1)")
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43 parser.add_option("-m", "--metadata", dest="metadataPath", type="string", default="./pipelineTest/metadata.tsv",help="absolute file path to reverse read (R2)")
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44 parser.add_option("-p", "--sensitive_data", dest="sensitivePath", type="string", default="", help="Spreadsheet (CSV) with sensitive metadata")
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45 parser.add_option("-c", "--sensitive_cols", dest="sensitiveCols", type="string", default="", help="CSV list of column names from sensitive metadata spreadsheet to use as labels on dendrogram")
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46 parser.add_option("-o", "--output_file", dest="outputFile", type="string", default="tree.png", help="Output graphics file. Use ending 'png', 'pdf' or 'svg' to specify file format.")
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47 parser.add_option("-b", "--bcid_column", dest="bcidCol", type="string", default="BCID", help="Column name of BCID in sensitive metadata file")
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48 parser.add_option("-n", "--missing_value", dest="naValue", type="string", default="NA", help="Value to write for missing data.")
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49
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50 (options,args) = parser.parse_args()
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51 treePath = str(options.treePath).lstrip().rstrip()
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52 distancePath = str(options.distancePath).lstrip().rstrip()
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53 metadataPath = str(options.metadataPath).lstrip().rstrip()
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54 sensitivePath = str(options.sensitivePath).lstrip().rstrip()
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55 sensitiveCols = str(options.sensitiveCols).lstrip().rstrip()
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56 outputFile = str(options.outputFile).lstrip().rstrip()
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57 bcidCol = str( str(options.bcidCol).lstrip().rstrip() )
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58 naValue = str( str(options.naValue).lstrip().rstrip() )
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59
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60 if len(sensitivePath) == 0:
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61 print("Must give a file with sensitive meta data. Option -p, or --sensitive_data")
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62
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63 ### test values to uncomment
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64 # sensitivePath = "./sensitive_metadata.csv"
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65 # sensitiveCols = "Name,Care facility"
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66 # outputFile = "newtree_test.png"
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67 # bcidCol = "BCID"
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68
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69
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70
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71 import pandas
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72 class SensitiveMetadata(object):
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73 def __init__(self):
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74 x = pandas.read_csv( sensitivePath )
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75 col_names = [ s for s in sensitiveCols.split(',')] # convert to 0 offset
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76 if not bcidCol in col_names:
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77 col_names.append( bcidCol )
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78 all_cols = [ str(col) for col in x.columns ]
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79 col_idxs = [ all_cols.index(col) for col in col_names ]
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80 self.sensitive_data = x.iloc[:, col_idxs]
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81 def get_columns(self):
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82 cols = [ str(x) for x in self.sensitive_data.columns ]
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83 return cols
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84 def get_value( self, bcid, column_name ): # might be nice to get them all in single call via an input list of bcids ... for later
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85 bcids= list( self.sensitive_data.loc[:, bcidCol ] ) # get the list of all BCIDs in sensitive metadata
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86 if not bcid in bcids:
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87 return naValue
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88 else:
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89 row_idx = bcids.index( bcid ) # lookup the row for this BCID
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90 return self.sensitive_data.loc[ row_idx, column_name ] # return the one value based on the column (col_idx) and this row
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91
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92
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93 #region result objects
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94 #define some objects to store values from results
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95 #//TODO this is not the proper way of get/set private object variables. every value has manually assigned defaults intead of specified in init(). Also, use property(def getVar, def setVar).
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96 class workflowResult(object):
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97 def __init__(self):
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98 self.new = False
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99 self.ID = ""
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100 self.ExpectedSpecies = ""
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101 self.MLSTSpecies = ""
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102 self.SequenceType = ""
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103 self.MLSTScheme = ""
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104 self.CarbapenemResistanceGenes =""
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105 self.OtherAMRGenes=""
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106 self.TotalPlasmids = 0
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107 self.plasmids = []
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108 self.DefinitelyPlasmidContigs =""
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109 self.LikelyPlasmidContigs=""
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110 self.row = ""
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111 class plasmidObj(object):
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112 def __init__(self):
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113 self.PlasmidsID = 0
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114 self.Num_Contigs = 0
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115 self.PlasmidLength = 0
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116 self.PlasmidRepType = ""
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117 self.PlasmidMobility = ""
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118 self.NearestReference = ""
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119
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120 #endregion
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121
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122 #region useful functions
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123 def read(path): #read in a text file to a list
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124 return [line.rstrip('\n') for line in open(path)]
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125 def execute(command): #subprocess.popen call bash command
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126 process = subprocess.Popen(command, shell=False, cwd=curDir, universal_newlines=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
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127
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128 # Poll process for new output until finished
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129 while True:
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130 nextline = process.stdout.readline()
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131 if nextline == '' and process.poll() is not None:
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132 break
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133 sys.stdout.write(nextline)
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134 sys.stdout.flush()
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135
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136 output = process.communicate()[0]
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137 exitCode = process.returncode
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138
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139 if (exitCode == 0):
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140 return output
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141 else:
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142 raise subprocess.CalledProcessError(exitCode, command)
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143 def httpGetFile(url, filepath=""): #download a file from the web
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144 if (filepath == ""):
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145 return urllib.request.urlretrieve(url)
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146 else:
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147 urllib.request.urlretrieve(url, filepath)
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148 return True
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149 def gunzip(inputpath="", outputpath=""): #gunzip
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150 if (outputpath == ""):
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151 with gzip.open(inputpath, 'rb') as f:
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152 gzContent = f.read()
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153 return gzContent
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154 else:
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155 with gzip.open(inputpath, 'rb') as f:
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156 gzContent = f.read()
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157 with open(outputpath, 'wb') as out:
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158 out.write(gzContent)
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159 return True
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160 def addFace(name): #function to add a facet to a tree
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161 #if its the reference branch, populate the faces with column headers
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162 face = e.faces.TextFace(name,fsize=10,tight_text=True)
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163 face.border.margin = 5
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164 face.margin_right = 5
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165 face.margin_left = 5
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166 return face
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167 #endregion
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168
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169 #region functions to parse result files
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170 def ParseWorkflowResults(pathToResult):
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171 _worflowResult = {}
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172 r = pandas.read_csv(pathToResult, delimiter='\t', header=0)
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173 r = r.replace(numpy.nan, '', regex=True)
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174 for i in range(len(r.index)):
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175 _results = workflowResult()
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176 if(str(r.loc[r.index[i], 'new']).lower() == "new"):
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177 _results.new = True
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178 else:
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179 _results.new = False
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180 _results.ID = str(r.loc[r.index[i], 'ID'])
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181 _results.ExpectedSpecies = str(r.loc[r.index[i], 'Expected Species'])
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182 _results.MLSTSpecies = str(r.loc[r.index[i], 'MLST Species'])
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183 _results.SequenceType = str(r.loc[r.index[i], 'Sequence Type'])
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184 _results.MLSTScheme = (str(r.loc[r.index[i], 'MLST Scheme']))
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185 _results.CarbapenemResistanceGenes = (str(r.loc[r.index[i], 'Carbapenem Resistance Genes']))
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186 _results.OtherAMRGenes = (str(r.loc[r.index[i], 'Other AMR Genes']))
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187 _results.TotalPlasmids = int(r.loc[r.index[i], 'Total Plasmids'])
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188 for j in range(0,_results.TotalPlasmids):
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189 _plasmid = plasmidObj()
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190 _plasmid.PlasmidsID =(((str(r.loc[r.index[i], 'Plasmids ID'])).split(";"))[j])
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191 _plasmid.Num_Contigs = (((str(r.loc[r.index[i], 'Num_Contigs'])).split(";"))[j])
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192 _plasmid.PlasmidLength = (((str(r.loc[r.index[i], 'Plasmid Length'])).split(";"))[j])
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193 _plasmid.PlasmidRepType = (((str(r.loc[r.index[i], 'Plasmid RepType'])).split(";"))[j])
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194 _plasmid.PlasmidMobility = ((str(r.loc[r.index[i], 'Plasmid Mobility'])).split(";"))[j]
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195 _plasmid.NearestReference = ((str(r.loc[r.index[i], 'Nearest Reference'])).split(";"))[j]
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196 _results.plasmids.append(_plasmid)
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197 _results.DefinitelyPlasmidContigs = (str(r.loc[r.index[i], 'Definitely Plasmid Contigs']))
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198 _results.LikelyPlasmidContigs = (str(r.loc[r.index[i], 'Likely Plasmid Contigs']))
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199 _results.row = "\t".join(str(x) for x in r.ix[i].tolist())
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200 _worflowResult[_results.ID] = _results
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201 return _worflowResult
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202
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203 #endregion
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204
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205 def Main():
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206 sensitive_meta_data = SensitiveMetadata()
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207 # print( sensitive_meta_data.get_columns() )
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208 metadata = ParseWorkflowResults(metadataPath)
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209 distance = read(distancePath)
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210 treeFile = "".join(read(treePath))
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211
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212 distanceDict = {} #store the distance matrix as rowname:list<string>
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213 for i in range(len(distance)):
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214 temp = distance[i].split("\t")
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215 distanceDict[temp[0]] = temp[1:]
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216 #region step5: tree construction
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217
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218 '''
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219 #region create detailed tree
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220
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221 plasmidCount = 0
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222 for n in t.traverse():
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223 if (n.is_leaf() and not n.name == "Reference"):
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224 mData = metadata[n.name.replace(".fa","")]
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225 face = faces.TextFace(mData.MLSTSpecies,fsize=10,tight_text=True)
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226 face.border.margin = 5
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227 face.margin_left = 10
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228 face.margin_right = 10
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229 n.add_face(face, 0, "aligned")
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230 face = faces.TextFace(mData.SequenceType,fsize=10,tight_text=True)
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231 face.border.margin = 5
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232 face.margin_right = 10
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233 n.add_face(face, 1, "aligned")
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234 face = faces.TextFace(mData.CarbapenemResistanceGenes,fsize=10,tight_text=True)
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235 face.border.margin = 5
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236 face.margin_right = 10
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237 n.add_face(face, 2, "aligned")
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238 index = 3
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239 if (mData.TotalPlasmids > plasmidCount):
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240 plasmidCount = mData.TotalPlasmids
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241 for i in range(0, mData.TotalPlasmids):
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242 face = faces.TextFace(mData.plasmids[i].PlasmidRepType,fsize=10,tight_text=True)
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243 face.border.margin = 5
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244 face.margin_right = 10
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245 n.add_face(face, index, "aligned")
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246 index+=1
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247 face = faces.TextFace(mData.plasmids[i].PlasmidMobility,fsize=10,tight_text=True)
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248 face.border.margin = 5
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249 face.margin_right = 10
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250 n.add_face(face, index, "aligned")
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251 index+=1
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252
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253 face = faces.TextFace("Species",fsize=10,tight_text=True)
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254 face.border.margin = 5
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255 face.margin_right = 10
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256 face.margin_left = 10
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257 (t&"Reference").add_face(face, 0, "aligned")
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258 face = faces.TextFace("Sequence Type",fsize=10,tight_text=True)
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259 face.border.margin = 5
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260 face.margin_right = 10
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261 (t&"Reference").add_face(face, 1, "aligned")
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262 face = faces.TextFace("Carbapenamases",fsize=10,tight_text=True)
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263 face.border.margin = 5
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264 face.margin_right = 10
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265 (t&"Reference").add_face(face, 2, "aligned")
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266 index = 3
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267 for i in range(0, plasmidCount):
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268 face = faces.TextFace("plasmid " + str(i) + " replicons",fsize=10,tight_text=True)
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269 face.border.margin = 5
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270 face.margin_right = 10
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271 (t&"Reference").add_face(face, index, "aligned")
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272 index+=1
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273 face = faces.TextFace("plasmid " + str(i) + " mobility",fsize=10,tight_text=True)
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274 face.border.margin = 5
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275 face.margin_right = 10
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276 (t&"Reference").add_face(face, index, "aligned")
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277 index+=1
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278
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279 t.render("./pipelineTest/tree.png", w=5000,units="mm", tree_style=ts)
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280
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281 #endregion
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282 '''
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283 #region create box tree
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284 #region step5: tree construction
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285 treeFile = "".join(read(treePath))
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286 t = e.Tree(treeFile)
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287 t.set_outgroup(t&"Reference")
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288
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289 #set the tree style
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290 ts = e.TreeStyle()
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291 ts.show_leaf_name = False
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292 ts.show_branch_length = True
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293 ts.scale = 2000 #pixel per branch length unit
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294 ts.branch_vertical_margin = 15 #pixel between branches
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295 style2 = e.NodeStyle()
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296 style2["fgcolor"] = "#000000"
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297 style2["shape"] = "circle"
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298 style2["vt_line_color"] = "#0000aa"
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299 style2["hz_line_color"] = "#0000aa"
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300 style2["vt_line_width"] = 2
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301 style2["hz_line_width"] = 2
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302 style2["vt_line_type"] = 0 # 0 solid, 1 dashed, 2 dotted
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303 style2["hz_line_type"] = 0
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304 for n in t.traverse():
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305 n.set_style(style2)
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306
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307 #find the plasmid origins
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308 plasmidIncs = {}
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309 for key in metadata:
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310 for plasmid in metadata[key].plasmids:
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311 for inc in plasmid.PlasmidRepType.split(","):
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312 if (inc.lower().find("inc") > -1):
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313 if not (inc in plasmidIncs):
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314 plasmidIncs[inc] = [metadata[key].ID]
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315 else:
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316 if metadata[key].ID not in plasmidIncs[inc]:
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317 plasmidIncs[inc].append(metadata[key].ID)
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318 #plasmidIncs = sorted(plasmidIncs)
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319 for n in t.traverse(): #loop through the nodes of a tree
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320 if (n.is_leaf() and n.name == "Reference"):
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321 #if its the reference branch, populate the faces with column headers
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322 index = 0
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323
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324 for sensitive_data_column in sensitive_meta_data.get_columns():
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325 (t&"Reference").add_face(addFace(sensitive_data_column), index, "aligned")
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326 index = index + 1
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327
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328 (t&"Reference").add_face(addFace("SampleID"), index, "aligned")
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329 index = index + 1
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330 (t&"Reference").add_face(addFace("New?"), index, "aligned")
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331 index = index + 1
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332 for i in range(len(plasmidIncs)): #this loop adds the columns (aka the incs) to the reference node
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333 (t&"Reference").add_face(addFace(list(plasmidIncs.keys())[i]), i + index, "aligned")
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334 index = index + len(plasmidIncs)
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335 (t&"Reference").add_face(addFace("MLSTScheme"), index, "aligned")
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336 index = index + 1
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337 (t&"Reference").add_face(addFace("Sequence Type"), index, "aligned")
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338 index = index + 1
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339 (t&"Reference").add_face(addFace("Carbapenamases"), index, "aligned")
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340 index = index + 1
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341 for i in range(len(distanceDict[list(distanceDict.keys())[0]])): #this loop adds the distance matrix
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342 (t&"Reference").add_face(addFace(distanceDict[list(distanceDict.keys())[0]][i]), index + i, "aligned")
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343 index = index + len(distanceDict[list(distanceDict.keys())[0]])
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344 elif (n.is_leaf() and not n.name == "Reference"):
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345 #not reference branches, populate with metadata
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346 index = 0
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347 mData = metadata[n.name.replace(".fa","")]
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348
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349 # pushing in sensitive data
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350 for sensitive_data_column in sensitive_meta_data.get_columns():
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351 sens_col_val = sensitive_meta_data.get_value(bcid=mData.ID, column_name=sensitive_data_column )
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352 n.add_face(addFace(sens_col_val), index, "aligned")
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353 index = index + 1
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354
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355 n.add_face(addFace(mData.ID), index, "aligned")
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356 index = index + 1
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357 if (metadata[n.name.replace(".fa","")].new == True): #new column
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358 face = e.RectFace(30,30,"green","green") # TextFace("Y",fsize=10,tight_text=True)
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359 face.border.margin = 5
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360 face.margin_right = 5
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361 face.margin_left = 5
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362 face.vt_align = 1
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363 face.ht_align = 1
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364 n.add_face(face, index, "aligned")
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365 index = index + 1
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366 for incs in plasmidIncs: #this loop adds presence/absence to the sample nodes
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367 if (n.name.replace(".fa","") in plasmidIncs[incs]):
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368 face = e.RectFace(30,30,"black","black") # TextFace("Y",fsize=10,tight_text=True)
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369 face.border.margin = 5
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370 face.margin_right = 5
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371 face.margin_left = 5
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372 face.vt_align = 1
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373 face.ht_align = 1
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374 n.add_face(face, list(plasmidIncs.keys()).index(incs) + index, "aligned")
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375 index = index + len(plasmidIncs)
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376 n.add_face(addFace(mData.MLSTSpecies), index, "aligned")
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377 index = index + 1
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378 n.add_face(addFace(mData.SequenceType), index, "aligned")
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379 index = index + 1
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380 n.add_face(addFace(mData.CarbapenemResistanceGenes), index, "aligned")
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381 index = index + 1
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382 for i in range(len(distanceDict[list(distanceDict.keys())[0]])): #this loop adds distance matrix
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383 n.add_face(addFace(list(distanceDict[n.name])[i]), index + i, "aligned")
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384
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385 t.render( outputFile, w=5000,units="mm", tree_style=ts) #save it as a png. or an phyloxml
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386
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387 #endregion
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388 #endregion
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389
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390
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391 start = time.time()#time the analysis
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392
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393 #analysis time
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394 Main()
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395
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396 end = time.time()
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397 print("Finished!\nThe analysis used: " + str(end-start) + " seconds") |
